Autonomous sensing method for tunnel full-section mechanical construction and related equipment

Abstract

The invention discloses an autonomous sensing method for tunnel full-section mechanical construction and related equipment, relates to the field of tunneling, and mainly aims to solve the problem that fault detection of tunneling equipment for tunnel full-section mechanical construction is not rapid and accurate enough. The method comprises the following steps: determining optimized operation data of tunneling equipment; dividing the optimized operation data based on a rough set theory to construct a fault diagnosis model which is an RBF neural network; and determining the fault type and position based on the fault diagnosis model so as to autonomously perceive the tunneling equipment. The method is used for the autonomous sensing process of tunnel full-section mechanical construction.

Description

Technical Field

[0001] The present invention relates to the field of tunnel excavation, and in particular to an autonomous sensing method and related equipment for mechanized construction of a tunnel with a full cross section. Background Art

[0002] Excavation equipment is the core equipment in the mechanized construction of the whole section of the tunnel, mainly including three major systems: mechanical, electrical and hydraulic. In the actual operation process, these systems are prone to various faults due to factors such as complex working environment, large load changes, and long operation time. Although traditional fault diagnosis methods, such as temperature monitoring and vibration monitoring, can reflect the operating status of the equipment to a certain extent, it is often difficult to quickly and accurately determine the cause of the fault due to the concurrency, correlation and uncertainty between faults. Summary of the invention

[0003] In view of the above problems, the present invention provides an autonomous perception method and related equipment for mechanized construction of full-section tunnels, the main purpose of which is to solve the problem that fault detection of tunneling equipment in mechanized construction of full-section tunnels is not fast and accurate enough.

[0004] In order to solve at least one of the above technical problems, in a first aspect, the present invention provides an autonomous perception method for mechanized construction of a full-section tunnel, the method comprising:

[0005] Determine optimal operating data for tunnel boring equipment;

[0006] Dividing the optimization operation data based on rough set theory to construct a fault diagnosis model, wherein the fault diagnosis model is a RBF neural network;

[0007] The fault type and location are determined based on the fault diagnosis model to autonomously sense the tunnel boring equipment.

[0008] Optionally, determining the optimized operation data of the tunnel boring equipment includes:

[0009] Obtain initial operating data of tunnel boring equipment;

[0010] The initial operation data is subjected to filtering preprocessing and / or denoising preprocessing to determine optimized operation data.

[0011] Optionally, dividing the optimization operation data based on rough set theory to construct a fault diagnosis model includes:

[0012] Performing attribute simplification operation on the optimization operation data based on the rough set theory;

[0013] Removing redundant attributes based on the attribute reduction operation to retain target attributes, wherein the target attributes are attributes required for fault diagnosis;

[0014] Extract fault diagnosis rules based on rough set theory;

[0015] A fault diagnosis model is formed based on the target attributes.

[0016] Optionally, determining the fault type and location based on the fault diagnosis model to autonomously sense the tunnel boring equipment includes:

[0017] Obtain real-time operating data of tunnel boring equipment;

[0018] Inputting the real-time operation data into the fault diagnosis model to obtain an output result;

[0019] The output results are classified to obtain the fault type and location.

[0020] Optional,

[0021] The RBF neural network structure includes an input layer, a hidden layer and an output layer.

[0022] The relationship between the input layer and the hidden layer is nonlinear.

[0023] The relationship from the hidden layer to the output layer is linear.

[0024] Optionally, the above method further includes:

[0025] Based on the fault diagnosis requirements, the RBF neural network is designed, wherein the input layer of the RBF neural network structure is the fault feature vector, the hidden layer is the radial basis function, and the output layer is the fault type.

[0026] Optionally, the above method further includes:

[0027] Initializing the center vector of the RBF neural network based on the K-means clustering algorithm;

[0028] Initializing the width parameter and weight of the RBF neural network using the least square method;

[0029] The RBF neural network is trained by gradient descent method.

[0030] In a second aspect, an embodiment of the present invention further provides an autonomous sensing device for mechanized construction of a tunnel full section, comprising:

[0031] A first determination unit, configured to determine optimized operation data of the tunnel boring equipment;

[0032] A partitioning unit, used for partitioning the optimization operation data based on rough set theory to construct a fault diagnosis model, wherein the fault diagnosis model is a RBF neural network structure;

[0033] The second determination unit is used to determine the fault type and location based on the fault diagnosis model to autonomously sense the tunnel boring equipment.

[0034] In order to achieve the above-mentioned purpose, according to the third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the above-mentioned program is executed by a processor, the steps of the above-mentioned autonomous perception method for full-section mechanized construction of a tunnel are implemented.

[0035] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present invention, there is provided an electronic device, comprising at least one processor and at least one memory connected to the processor; wherein the above-mentioned processor is used to call the program instructions in the above-mentioned memory to execute the steps of the above-mentioned autonomous perception method for full-section mechanized construction of a tunnel.

[0036] By means of the above technical scheme, the present invention provides an autonomous perception method and related equipment for mechanized construction of the full section of a tunnel. For the problem that the fault detection of the tunneling equipment of the mechanized construction of the full section of a tunnel is not fast and accurate enough, the present invention determines the optimized operation data of the tunneling equipment; divides the optimized operation data based on the rough set theory to construct a fault diagnosis model, wherein the fault diagnosis model is an RBF neural network; determines the fault type and location based on the fault diagnosis model to autonomously perceive the tunneling equipment. In the above scheme, a fault diagnosis method based on the combination of RBF neural network and rough set is proposed. Through the fault diagnosis system, autonomous perception and intelligent control of the operating state of the tunneling equipment can be realized, the operating efficiency and safety of the equipment can be improved, the concurrency, correlation and uncertainty between faults can be effectively handled, and the accuracy of fault diagnosis can be improved to achieve rapid and accurate fault diagnosis of the tunneling equipment, and finally realize autonomous perception of mechanized construction of the full section of the tunnel. The above-mentioned fault diagnosis method based on the combination of RBF neural network and rough set is not only applicable to tunneling equipment, but also can be promoted and applied to other large-scale mechanical equipment, such as mining machinery, engineering machinery, etc.

[0037] Correspondingly, the autonomous sensing device, equipment and computer-readable storage medium for full-section mechanized construction of a tunnel provided by the embodiments of the present invention also have the above-mentioned technical effects.

[0038] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0040] Figure 1 A schematic flow chart of an autonomous sensing method for mechanized construction of a tunnel full section provided by an embodiment of the present invention is shown;

[0041] Figure 2 A schematic block diagram showing the composition of an autonomous sensing device for mechanized construction of a tunnel full section provided by an embodiment of the present invention;

[0042] Figure 3 A schematic block diagram showing the composition of an autonomous sensing electronic device for mechanized construction of a full-section tunnel provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0044] In order to solve the problem that the fault detection of tunneling equipment in full-section mechanized construction of tunnels is not fast and accurate enough, an embodiment of the present invention provides an autonomous perception method for full-section mechanized construction of tunnels, such as Figure 1 As shown, the method includes:

[0045] S101, determining optimized operation data of tunnel boring equipment;

[0046] In one embodiment, initial operation data of the tunnel boring equipment is acquired; and filtering preprocessing and / or denoising preprocessing are performed on the initial operation data to determine optimized operation data.

[0047] Exemplarily, the present application collects the operation data of the tunneling equipment in real time through sensors, including parameters such as temperature, vibration, pressure, current, etc. The collected data is pre-processed by filtering, denoising, etc. to improve the quality of the data.

[0048] Specifically, various sensors are arranged at key positions of the tunneling equipment, such as temperature sensors, vibration sensors, pressure sensors and current sensors, to ensure that the operating data of the equipment can be fully collected.

[0049] S102, dividing the optimization operation data based on rough set theory to construct a fault diagnosis model, wherein the fault diagnosis model is a RBF neural network;

[0050] In one embodiment, an attribute simplification operation is performed on the optimization operation data based on the rough set theory; redundant attributes are removed based on the attribute simplification operation to retain target attributes, wherein the target attributes are attributes required for fault diagnosis; fault diagnosis rules are extracted based on the rough set theory; and a fault diagnosis model is formed based on the target attributes.

[0051] Exemplarily, rough set theory is used to simplify the attributes of the collected fault data, remove redundant attributes, and retain the attributes most useful for fault diagnosis. Fault diagnosis rules are extracted through rough set theory to form a preliminary fault diagnosis model.

[0052] Specifically, digital filtering technology is used to filter the collected data, remove high-frequency noise and interference signals, and retain effective fault characteristics. The collected data is normalized to eliminate the dimensional differences between different parameters and improve the comparability of the data.

[0053] Based on the above scheme, the rough set theory is used to remove redundant attributes, extract effective fault diagnosis rules, and combine the nonlinear mapping ability of RBF neural network to accurately identify the fault type and location. Rough set theory can effectively handle the concurrency and correlation between faults, simplify the fault diagnosis process, and reduce the difficulty of diagnosis.

[0054] In one embodiment, the RBF neural network structure includes an input layer, a hidden layer and an output layer. The relationship from the input layer to the hidden layer is nonlinear, and the relationship from the hidden layer to the output layer is linear.

[0055] Exemplarily, according to the requirements of fault diagnosis, the structure of the RBF neural network is designed, including an input layer, a hidden layer and an output layer. ωm is the name of the path from the hidden layer to the output layer.

[0056] Data conversion module No. 1 and data conversion module No. 2 are connected to the intelligent analysis and control terminal device respectively, and they communicate and connect through the F5G communication transmission control protocol. Data conversion module No. 1 serializes the data collected by the automatic sensing device, and the intelligent analysis and control terminal server deserializes the data and stores it in the corresponding database. Multi-source data transmission based on F5G is divided into real-time data transmission and historical data transmission. The preset time interval of real-time data transmission is not limited and can be set to any reasonable time interval. Usually, the time interval is set to 5s or 10s. Real-time data obtains the latest data from the cached memory according to the set time interval, and directly transmits it to the intelligent analysis and control terminal through the multi-source data transmission system based on F5G. Historical data queries a specified number of data from the cached database, and sends them to the intelligent analysis and control terminal in the order of old and new time. At the same time, the corresponding data is deleted from the cache. When the F5G network is temporarily interrupted for some reason, the historical data will be processed by the intelligent station to process the multi-source information; the decision-making application device mainly receives the instructions from the No. 2 data conversion device and performs the corresponding action.

[0057] This application takes into account the fast convergence characteristics of RBF neural networks, and can complete fault diagnosis in a short time to meet the needs of real-time monitoring.

[0058] In one embodiment, the method further includes: designing the RBF neural network based on fault diagnosis requirements, wherein the input layer of the RBF neural network structure is the fault feature vector, the hidden layer is the radial basis function, and the output layer is the fault type.

[0059] Exemplarily, according to the requirements of fault diagnosis, the structure of the RBF neural network is designed, the input layer is the fault feature vector, the hidden layer is the radial basis function, and the output layer is the fault type.

[0060] In one embodiment, the method further includes: initializing the center vector of the RBF neural network based on a K-means clustering algorithm; initializing the width parameter and weight of the RBF neural network using a least squares method; and training the RBF neural network using a gradient descent method.

[0061] Exemplarily, the parameters of the RBF neural network are initialized, including the center vector, width parameter and weight. The present application uses the preprocessed fault data to train the RBF neural network, optimize the network parameters, and improve the generalization ability of the network.

[0062] Specifically, the K-means clustering algorithm is used to initialize the center vector of the RBF neural network, and the least square method is used to initialize the width parameter and weight. The gradient descent method is used to train the RBF neural network, optimize the network parameters, and improve the generalization ability of the network.

[0063] S103: Determine the fault type and location based on the fault diagnosis model to autonomously sense the tunnel boring equipment.

[0064] In one embodiment, real-time operation data of the tunnel boring equipment is obtained; the real-time operation data is input into the fault diagnosis model to obtain output results; and the output results are classified to obtain the fault type and location.

[0065] For example, the real-time collected equipment operation data is input into the trained RBF neural network for fault diagnosis. According to the output of the neural network, the fault is classified to determine the fault type and location.

[0066] In one embodiment, the present application realizes autonomous perception of the operating status of the tunneling equipment through a fault diagnosis system, monitors the health status of the equipment in real time, and automatically adjusts the operating parameters of the equipment or issues an early warning to the operator based on the fault diagnosis results to prevent the fault from further deteriorating.

[0067] Based on the above solution, the fault diagnosis system can realize autonomous perception of the operating status of the tunneling equipment, and intelligent control can be performed according to the diagnosis results to improve the operating efficiency and safety of the equipment. Accurate fault diagnosis can timely discover potential problems of the equipment, avoid the expansion of faults, and reduce the maintenance cost and downtime of the equipment.

[0068] By means of the above technical scheme, the autonomous perception method for mechanized construction of the full section of the tunnel provided by the present invention is to solve the problem that the fault detection of the tunneling equipment of the mechanized construction of the full section of the tunnel is not fast and accurate enough. The present invention determines the optimized operation data of the tunneling equipment; divides the optimized operation data based on the rough set theory to construct a fault diagnosis model, wherein the fault diagnosis model is an RBF neural network; determines the fault type and location based on the fault diagnosis model to autonomously perceive the tunneling equipment. In the above scheme, a fault diagnosis method based on the combination of RBF neural network and rough set is proposed. Through the fault diagnosis system, autonomous perception and intelligent control of the operating state of the tunneling equipment can be realized, the operating efficiency and safety of the equipment can be improved, the concurrency, correlation and uncertainty between faults can be effectively handled, and the accuracy of fault diagnosis can be improved. It aims to realize fast and accurate fault diagnosis of tunneling equipment, and finally realize autonomous perception of mechanized construction of the full section of the tunnel. The above-mentioned fault diagnosis method based on the combination of RBF neural network and rough set is not only applicable to tunneling equipment, but also can be promoted and applied to other large-scale mechanical equipment, such as mining machinery, engineering machinery, etc.

[0069] Furthermore, as a response to the above Figure 1In order to realize the method shown in the figure, the embodiment of the present invention also provides an autonomous sensing device for mechanized construction of the whole section of a tunnel, which is used to Figure 1 The device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Figure 2 As shown, the device includes: a first determining unit 21, a dividing unit 22 and a second determining unit 23, wherein

[0070] A first determination unit 21, for determining optimized operation data of the tunnel boring equipment;

[0071] A partitioning unit 22, used for partitioning the optimization operation data based on rough set theory to construct a fault diagnosis model, wherein the fault diagnosis model is a RBF neural network structure;

[0072] The second determination unit 23 is used to determine the fault type and location based on the fault diagnosis model to perform autonomous perception of the tunnel boring equipment.

[0073] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and an autonomous perception method for mechanized construction of a tunnel full section can be implemented by adjusting kernel parameters, which can solve the problem that fault detection of tunneling equipment for mechanized construction of a tunnel full section is not fast and accurate enough.

[0074] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, and when the program is executed by a processor, it implements the autonomous perception method for the mechanized construction of the entire section of the tunnel.

[0075] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes the autonomous perception method for mechanized construction of the entire section of the tunnel when running.

[0076] An embodiment of the present invention provides an electronic device, the electronic device comprising at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the autonomous perception method for mechanized construction of a tunnel full section as described above

[0077] An embodiment of the present invention provides an electronic device 30, such as Figure 3As shown, the electronic device includes at least one processor 301, and at least one memory 302 and a bus 303 connected to the processor; wherein the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call the program instructions in the memory to execute the above-mentioned autonomous perception method for full-section mechanized construction of the tunnel.

[0078] The intelligent electronic devices in this article can be PCs, PADs, mobile phones, etc.

[0079] The present application also provides a computer program product, which, when executed on a process management electronic device, is suitable for executing a program that initializes the above-mentioned autonomous perception method steps for full-section mechanized construction of a tunnel.

[0080] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0081] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0082] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0083] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0085] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 This corresponds to the flow of control of the memory in the embodiment.

[0086] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0087] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0088] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0089] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0091] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0092] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An autonomous perception method for mechanized construction of a tunnel full section, characterized in that: include: Determine optimal operating data for tunnel boring equipment; Dividing the optimization operation data based on rough set theory to construct a fault diagnosis model, wherein the fault diagnosis model is a RBF neural network; The fault type and location are determined based on the fault diagnosis model to autonomously sense the tunnel boring equipment.

2. The method according to claim 1, characterized in that Determining the optimized operation data of the tunnel boring equipment includes: Obtain initial operating data of tunnel boring equipment; The initial operation data is subjected to filtering preprocessing and / or denoising preprocessing to determine optimized operation data.

3. The method according to claim 1, characterized in that The method of dividing the optimization operation data based on rough set theory to construct a fault diagnosis model includes: Performing attribute simplification operation on the optimization operation data based on the rough set theory; Removing redundant attributes based on the attribute reduction operation to retain target attributes, wherein the target attributes are attributes required for fault diagnosis; Extract fault diagnosis rules based on rough set theory; A fault diagnosis model is formed based on the target attributes.

4. The method according to claim 1, characterized in that: Determining the fault type and location based on the fault diagnosis model to autonomously sense the tunnel boring equipment includes: Obtain real-time operating data of tunnel boring equipment; Inputting the real-time operation data into the fault diagnosis model to obtain an output result; The output results are classified to obtain the fault type and location.

5. The method according to claim 1, characterized in that The RBF neural network structure includes an input layer, a hidden layer and an output layer. The relationship between the input layer and the hidden layer is nonlinear. The relationship from the hidden layer to the output layer is linear.

6. The method according to claim 1, characterized in that Also includes: Based on the fault diagnosis requirements, the RBF neural network is designed, wherein the input layer of the RBF neural network structure is the fault feature vector, the hidden layer is the radial basis function, and the output layer is the fault type.

7. The method according to claim 6, characterized in that Also includes: Initializing the center vector of the RBF neural network based on the K-means clustering algorithm; Initializing the width parameter and weight of the RBF neural network using the least square method; The RBF neural network is trained by gradient descent method.

8. An autonomous sensing device for mechanized construction of a tunnel, characterized in that: Also includes: A first determination unit, configured to determine optimized operation data of the tunnel boring equipment; A partitioning unit, used for partitioning the optimization operation data based on rough set theory to construct a fault diagnosis model, wherein the fault diagnosis model is a RBF neural network structure; The second determination unit is used to determine the fault type and location based on the fault diagnosis model to autonomously sense the tunnel boring equipment.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the steps of the autonomous perception method for full-section mechanized construction of a tunnel as described in any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call the program instructions in the memory to execute the steps of the autonomous perception method for full-section mechanized construction of a tunnel as described in any one of claims 1 to 7.

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